# 3.1. Overall

A model has two parts, a descriptor that maps atomic configuration to a set of symmetry invariant features, and a fitting net that takes descriptor as input and predicts the atomic contribution to the target physical property. It’s defined in the model section of the input.json, for example

    "model": {
"type_map":	["O", "H"],
"descriptor" :{
"...": "..."
},
"fitting_net" : {
"...": "..."
}
}


Assume that we are looking for a model for water, we will have two types of atoms. The atom types are recorded as integers. In this example, we denote 0 for oxygen and 1 for hydrogen. A mapping from the atom type to their names is provided by type_map.

The model has two subsections descritpor and fitting_net, which defines the descriptor and the fitting net, respectively. The type_map is optional, which provides the element names (but not necessarily to be the element name) of the corresponding atom types.

DeePMD-kit implements the following descriptors:

1. se_e2_a: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes the distance between atoms as input.

2. se_e2_r: DeepPot-SE constructed from radial information of atomic configurations. The embedding takes the distance between atoms as input.

3. se_e3: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes angles between two neighboring atoms as input.

4. loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.

5. hybrid: Concate a list of descriptors to form a new descriptor.

The fitting of the following physical properties are supported

1. ener: Fitting the energy of the system. The force (derivative with atom positions) and the virial (derivative with the box tensor) can also be trained. See the example.

2. dipole: The dipole moment.

3. polar: The polarizability.